Abstract
Infections acquired in healthcare settings (nosocomial infections) have become one of the main health problems in acute care centers. Some of epidemiologists’ efforts are focused on studying patient’s traceability and determining the main factors that lead to its appearance. However, specialists demand new technology to ease such analysis.
In this work, we explore the capacity of alternative technologies in information storage, like Graph databases (GDBs). GDBs, unlike the traditional (relational) databases present in Information Health Systems, have a remarkable expressiveness for modeling and querying highly inter-liked concepts in data-sets. In particular, we focus on the study of the advantages GDBs can offer in the analysis of contacts between patients diagnosed with a bacterial nosocomial infection in a hospital setting.
The contributions of our research are the following: a design and implementation of the domain has been carried out, with the ability to model any hospital architectural structure on several levels, as well as represent the clinical events associated with patients, thus contemplating a spatial and temporal modeling. Finally, we study the query expressiveness and performance for the analysis of contacts in infection spread.
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Notes
- 1.
OrientDB: https://orientdb.com/.
- 2.
ARANGO: https://www.arangodb.com/.
- 3.
Neo4J: https://neo4j.com/.
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Acknowledgments
This work was partially funded by the SITSUS project (Ref: RTI2018-094832-B-I00), given by the Spanish Ministry of Science, Innovation and Universities (MCIU), the Spanish Agency for Research (AEI) and by the European Fund for Regional Development (FEDER).
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Pujante, L., Campos, M., Juarez, J.M., Canovas-Segura, B., Morales, A. (2020). Graph Databases for Contact Analysis in Infections Using Spatial Temporal Models. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2020. Lecture Notes in Computer Science(), vol 12108. Springer, Cham. https://doi.org/10.1007/978-3-030-45385-5_9
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DOI: https://doi.org/10.1007/978-3-030-45385-5_9
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